Generative & Agentic AI
Generative AI and RAG Applications
G01 · Practitioner
Entry requirements
F01; basic APIs
What 36 hours means
- Lessons
- 24 h
- Capstone
- 8 h
- Assessment
- 2 h
- Remediation
- 2 h
Syllabus
Four modules, twelve lessons. Every lesson states what it covers and the evidence you produce for it.
Module 1
Model interaction
Language model fundamentals · Prompt design · Structured outputs
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G01.01 · 120 min
Language model fundamentals
Explain tokens, context, sampling and common failure modes.
Evidence: A short experiment connects settings to measured output differences.
G01.02 · 120 min
Prompt design
Separate instructions, context and user input; specify output constraints.
Evidence: A prompt passes a fixed set of normal and ambiguous requests.
G01.03 · 120 min
Structured outputs
Module 2
Grounding
Document ingestion · Retrieval integration · Grounded responses
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G01.04 · 120 min
Document ingestion
Parse approved sources and preserve citation metadata.
Evidence:
Module 3
Evaluation
Evaluation datasets · Quality evaluation · Security and privacy tests
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G01.07 · 120 min
Evaluation datasets
Build answerable, unanswerable and adversarial test cases.
Evidence:
Module 4
Application delivery
Latency and cost · Application experience · Release and monitoring
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G01.10 · 120 min
Latency and cost
Measure token use, retrieval time and total response time.
Evidence:
Capstone
Build a source-grounded course information assistant using approved documents, with citations, refusal tests, evaluation and a cost report.
Lab environment
AI API sandbox
Lab infrastructure is not yet provisioned, so this describes the environment the course is designed for rather than one you can launch today.